Considering the popularity of smart mobile devices, it is extremely important to build a lightweight and efficient SID model.
As shown in Figure REF , we find that some noise in the noisy image can be eliminated by using the simple down- and up-sampling operations.
However, we also notice that the downsampling operation also results in the loss of useful information when removing noise.
Meanwhile, the bigger the downsampling rate, the more noise will be removed, but the reconstructed image will become more blurred.
To solve the aforementioned problem, we propose a Multiple Degradation and Reconstruction Network (MDRN) to progressively remove image noise inspired by Unet++ ([1]}).
Meanwhile, Multi-scale Aggregation Block (MSAB) and Multi-scale Aggregation Group (MSAG) are specially designed for multi-scale feature extraction.
Meanwhile, we propose two novel Heterogeneous Knowledge Distillation Strategies (HKDS) to enable MDRN to learn richer and more accurate features from the teacher model, thereby further improving the model performance.
In other words, the model uses the knowledge provided by external heterogeneous models to realize the automatic learning of deep feature priors. In summary, the contributions of this paper are as follows:
